203 citations · 2.2k across the 52 of their papers we have counts for
7 papers · 1 filter
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Simo Alami. C, Fernando Llorente, Rim Kaddah +2
Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal…
Inference over radiative transfer models using variational and expectation maximization methods
Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino +3
Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the…
Living in the Physics and Machine Learning Interplay for Earth Observation
Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés +7
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables…
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers
Luca Martino, Jesse Read
The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of…
Active emulation of computer codes with Gaussian processes -- Application to remote sensing
Daniel Heestermans Svendsen, Luca Martino, Gustau Camps-Valls
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest.…
Multi-label Methods for Prediction with Sequential Data
Jesse Read, Luca Martino, Jaakko Hollmén
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of cla…
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains
Jesse Read, Luca Martino, David Luengo
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional…